Scheduling Monotone Moldable Jobs in Linear Time

Jansen, Klaus; Land, Felix

doi:10.1109/ipdps.2018.00027

Cited by 26 publications

(21 citation statements)

References 22 publications

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“…Using the same techniques, they later improved the approximation ratio to 1.5 + [28]. Jansen and Land [20] showed the same 1.5 + ratio but with a lower runtime complexity as well as a PTAS, when the execution time functions of the jobs admit certain compact encodings.…”

Section: Monotonic Modelmentioning

confidence: 87%

Resilient Scheduling of Moldable Jobs on Failure-Prone Platforms

Benoît

Fèvre

Perotin

et al. 2020

2020 IEEE International Conference on Cluster Computing (CLUSTER)

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This paper focuses on the resilient scheduling of moldable parallel jobs on high-performance computing (HPC) platforms. Moldable jobs allow for choosing a processor allocation before execution, and their execution time obeys various speedup models. The objective is to minimize the overall completion time of the jobs, or makespan, assuming that jobs are subject to arbitrary failure scenarios, and hence need to be re-executed each time they fail until successful completion. This work generalizes the classical framework where jobs are known offline and do not fail. We introduce a list-based algorithm, and prove new approximation ratios for three prominent speedup models (roofline, communication, Amdahl). We also introduce a batch-based algorithm, where each job is allowed a restricted number of failures per batch, and prove a new approximation ratio for the arbitrary speedup model. We conduct an extensive set of simulations to evaluate and compare different variants of the two algorithms. The results show that they consistently outperform some baseline heuristics. In particular, the list algorithm performs better for the roofline and communication models, while the batch algorithm has better performance for the Amdahl's model. Overall, our best algorithm is within a factor of 1.47 of a lower bound on average over the whole set of experiments, and within a factor of 1.8 in the worst case.

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Section: Monotonic Modelmentioning

confidence: 87%

Resilient Scheduling of Moldable Jobs on Failure-Prone Platforms

Benoît

Fèvre

Perotin

et al. 2020

2020 IEEE International Conference on Cluster Computing (CLUSTER)

View full text Add to dashboard Cite

show abstract

“…Using the same techniques, they later improved the approximation ratio to 1.5 + [34]. Jansen and Land [21] showed the same 1.5 + ratio but with a lower runtime complexity, when the execution time functions of the jobs admit certain compact encodings. They also proposed a PTAS for the problem.…”

Section: Offline Scheduling Of Independent Moldable Jobsmentioning

confidence: 88%

Resilient Scheduling of Moldable Parallel Jobs to Cope With Silent Errors

Benoît

Fèvre

Perotin

et al. 2022

IEEE Trans. Comput.

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We study the resilient scheduling of moldable parallel jobs on highperformance computing (HPC) platforms. Moldable jobs allow for choosing a processor allocation before execution, and their execution time obeys various speedup models. The objective is to minimize the overall completion time of the jobs, or the makespan, when jobs can fail due to silent errors and hence may need to be re-executed after each failure until successful completion. Our work generalizes the classical scheduling framework for failure-free jobs. To cope with silent errors, we introduce two resilient scheduling algorithms, Lpa-List and Batch-List, both of which use the List strategy to schedule the jobs. Without knowing a priori how many times each job will fail, Lpa-List relies on a local strategy to allocate processors to the jobs, while Batch-List schedules the jobs in batches and allows only a restricted number of failures per job in each batch. We prove new approximation ratios for the two algorithms under several prominent speedup models (e.g., roofline, communication, Amdahl, power, monotonic, and a mixed model). An extensive set of simulations is conducted to evaluate different variants of the two algorithms, and the results show that they consistently outperform some baseline heuristics. Overall, our best algorithm is within a factor of 1.6 of a lower bound on average over the entire set of experiments, and within a factor of 4.2 in the worst case.

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“…Constantfactor approximation algorithms for the problem are known since the work of Turek et al (1992). A line of work establishing improved approximation results (Mounie et al, 1999;Jansen and Porko-lab, 2002;Mounié et al, 2007) recently culminated in a polynomial-time approximation scheme, implicit in the combined work of Jansen and Thöle (2010) and Jansen and Land (2018). Thus, the minimum makespan can be approximated efficiently up to arbitrary precision in the identical machine case, matching the complexity of the corresponding non-malleable scheduling problem P ||C max (Hochbaum and Shmoys, 1987).…”

Section: The Malleable Scheduling Modelmentioning

confidence: 99%

Assigning and Scheduling Generalized Malleable Jobs under Subadditive or Submodular Processing Speeds

Fotakis¹,

Matuschke²,

Papadigenopoulos³

2021

Preprint

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Malleable scheduling is a model that captures the possibility of parallelization to expedite the completion of time-critical tasks. A malleable job can be allocated and processed simultaneously on multiple machines, occupying the same time interval on all these machines. We study a general version of this setting, in which the functions determining the joint processing speed of machines for a given job follow different discrete concavity assumptions. As we show, when the processing speeds are fractionally subadditive, the problem of scheduling malleable jobs at minimum makespan can be approximated by a considerably simpler assignment problem. Moreover, we provide efficient approximation algorithms, with a logarithmic approximation factor for the case of submodular processing speeds, and a constant approximation factor when processing speeds are determined by matroid rank functions. Computational experiments indicate that our algorithms outperform the theoretical worst-case guarantees.

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Scheduling Monotone Moldable Jobs in Linear Time

Cited by 26 publications

References 22 publications

Resilient Scheduling of Moldable Jobs on Failure-Prone Platforms

Resilient Scheduling of Moldable Jobs on Failure-Prone Platforms

Resilient Scheduling of Moldable Parallel Jobs to Cope With Silent Errors

Assigning and Scheduling Generalized Malleable Jobs under Subadditive or Submodular Processing Speeds

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